Measuring inequities in transportation injuries in a Canadian commuter cohort: impacts of individual versus neighbourhood income
Bibliographic record
Abstract
Abstract Background Low income has been associated with a higher risk of transportation-related injury however, previous studies have largely relied on area-level income, due to the limited availability of individual-level data. Methods To examine the independent and combined roles of individual- and area-level income, this prospective cohort study followed ~ 6,557,000 Canadians from the Canadian Census Health and Environment Cohorts (2006, 2011, 2016), for pedestrian, bicycling, or motor vehicle hospitalizations. Income was measured (1) individually by the low-income cut-off and (2) at the area level using neighbourhood income quintiles. Poisson regression estimated the incidence rate ratios (IRR) and 95% confidence intervals (CI) for transportation-related hospitalizations. Results After adjusting for covariates, low-income individuals had higher risks of hospitalizations for pedestrian (IRR = 1.93, 95%CI (1.62, 2.29)), bicycling (IRR = 1.16, 95%CI (1.01, 1.34)) and motor vehicle injuries (IRR = 1.18, 95%CI (1.06, 1.31)). When both individual and neighbourhood income were assessed together we estimated, that those who lived in the lowest income neighbourhoods (compared to the highest) had a higher risk of pedestrian (IRR = 1.80, 95%CI (1.51, 2.14)) and motor vehicle injury (IRR = 1.33, 95%CI (1.22, 1.42)) but lower risk of bicycling injury (IRR = 0.73, 95%CI (0.65, 0.81)). Conclusions The interaction between individual and neighbourhood income revealed an increased injury risk for low-income individuals in all neighbourhoods, with large inequities in pedestrian and motor vehicle injury risk persisting even in the highest-income neighbourhoods. These findings demonstrate individual income independently contributes to transportation injury risk, underscoring the importance of considering both individual- and area-level income.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.173 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".